Abstract
In a previous paper we introduced a framework for combining Genetic Algorithms with ILP which included a novel representation for clauses and relevant operators. In this paper we complete the proposed framework by introducing a fast evaluation mechanism. In this evaluation mechanism individuals can be evaluated at genotype level (i.e. bit-strings) without mapping them into corresponding clauses. This is intended to replace the complex task of evaluating clauses (which usually needs repeated theorem proving) with simple bitwise operations. In this paper we also provide an experimental evaluation of the proposed framework. The results suggest that this framework could lead to significantly increased efficiency in problems involving complex target theories.
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References
C. Anglano, A. Giordana, G. Lo Bello, and L. Saitta. An experimental evaluation of coevolutive concept learning. pages 19–27. Morgan Kaufmann, 1998.
I. Bratko, S. Muggleton, and A. Varsek. Learning qualitative models of dynamic systems. In Proceedings of the Eighth International Machine Learning Workshop, San Mateo, Ca, 1991. Morgan-Kaufmann.
L. Davis. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991.
C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming. Academic Press, London, 1992.
A. Giordana and F. Neri. Search-intensive concept induction. Evolutionary Computation Journal, 3(4):375–416, 1996.
A. Giordana and C. Sale. Learning structured concepts using genetic algorithms. pages 169–178. Morgan Kaufmann, 1992.
D. E._Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, MA, 1989.
J. Hekanaho. Dogma: A ga-based relational learner. pages 205–214. Springer-Verlag, 1998.
C. Z. Janikow. A knowledge-intensive genetic algorithm for supervised learning. Machine Learning, 13:189–228, 1993.
Kenneth A. De Jong, William M. Spears, and Diana F. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13:161–188, 1993.
C. J. Kennedy and C. Giraud-Carrier. An evolutionary approach to concept learning with structured data. In Proceedings of the fourth International Conference on Artificial Neural Networks and Genetic Algorithms, pages 1–6. Springer Verlag, April 1999.
J. R. Koza. Genetic Programming. MIT Press, Cambridge, MA, 1991.
K. S. Leung and M. L. Wong. Genetic logic programming and applications. IEEE Expert, 10(5):68–76, 1995.
R.S. Michalski. Pattern recognition as rule-guided inductive inference. In Proceedings of IEEE Trans. on Pattern Analysis and Machine Intelligence, pages 349–361, 1980.
T.M. Mitchell. Generalisation as search. Artificial Intelligence, 18:203–226, 1982.
T. Mitchell. Machine Learning. McGraw-Hill, New York, 1997.
S.H. Muggleton. Stochastic logic programs. In L. de Raedt, editor, Advances in Inductive Logic Programming, pages 254–264. IOS Press, 1996.
S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, Tokyo, 1990. Ohmsha.
S. Muggleton, R. King, and M. Sternberg. Protein secondary structure prediction using logic-based machine learning. Protein Engineering, 5(7):647–657, 1992.
S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13:245–286, 1995.
S-H. Nienhuys-Cheng and R. de Wolf. Foundations of Inductive Logic Programming. Springer-Verlag, Berlin, 1997. LNAI 1228.
G.D. Plotkin. A note on inductive generalisation. In B. Meltzer and D. Michie, editors, Machine Intelligence 5, pages 153–163. Edinburgh University Press, Edinburgh, 1969.
P. G. K. Reiser and P. J. Riddle. Evolving logic programs to classify chess-endgame positions. In C. Newton, editor, Second Asia-Pacific Conference on Simulated Evolution and Learning, Canberra, Australia, 1998.
S F Smith. Flexible learning of problem solving heuristics through adaptive search. In Proc. 8th Int. Joint Conf. on A.I., pages 422–425, 1983.
J. Stender. Parallel Genetic Algorithms: Theory and Practice. IOS Press, Amsterdam,1993.
A. Tamaddoni-Nezhad and S. H. Muggleton. Searching the subsumption lattice by a genetic algorithm. In J. Cussens and A. Frisch, editors, Proceedings of the 10th International Conference on Inductive Logic Programming, pages 243–252. Springer-Verlag, 2000.
A. Varšek. Inductive Logic Programming with Genetic Algorithms. PhD thesis, Faculty of Electrical Engineering and Computer Science, University of Ljubljana, Ljubljana, Slovenia, 1993.
P. H. Winston. Learning Structural Descriptions from Examples. Phd thesis, MIT, Cambridge, Massachusetts, January 1970.
M. L. Wong and K. S. Leung. Evolutionary program induction directed by logic grammars. Evolutionary Computation, 5(2):143–180, 1997.
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Tamaddoni-Nezhad, A., Muggleton, S. (2003). A Genetic Algorithms Approach to ILP. In: Matwin, S., Sammut, C. (eds) Inductive Logic Programming. ILP 2002. Lecture Notes in Computer Science(), vol 2583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36468-4_19
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DOI: https://doi.org/10.1007/3-540-36468-4_19
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